How to Reduce Bias in the Life Cycle of a Data Science Project

Abstract Data science has become increasingly popular as a methodological approach for advancing theory and practice. However, significant concerns exist as to how bias can manifest within data science projects. Using a gender and leadership project as an example, we discuss how and when bias can emerge through the life cycle of a data science project. Specifically, after acknowledging potential structural biases, we identify and examine four key stages where bias is likely to emerge: (1) bias in the representation of data and the labeling process; (2) bias in algorithmic modeling; (3) bias in causal inferences; (4) bias in interpretation and application of results to inform policy and practice. In each section, we provide solutions for counteracting and reducing biases. These actionable recommendations serve to help researchers prevent, recognize, and/or reduce bias when it occurs in a data science project.